Wearable Seismocardiography‐Based Assessment of Stroke Volume in Congenital Heart Disease

Author:

Ganti Venu G.1ORCID,Gazi Asim H.2ORCID,An Sungtae3ORCID,Srivatsa Adith V.4ORCID,Nevius Brandi N.5ORCID,Nichols Christopher J.4ORCID,Carek Andrew M.67ORCID,Fares Munes8ORCID,Abdulkarim Mubeena8ORCID,Hussain Tarique8,Greil F. Gerald8ORCID,Etemadi Mozziyar67ORCID,Inan Omer T.12ORCID,Tandon Animesh89ORCID

Affiliation:

1. Bioengineering Graduate Program Georgia Institute of Technology Atlanta GA

2. School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA

3. School of Interactive Computing Georgia Institute of Technology Atlanta GA

4. The Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Atlanta GA

5. School of Mechanical Engineering Georgia Institute of Technology Atlanta GA

6. Department of Biomedical Engineering, McCormick School of Engineering Northwestern University Evanston IL

7. Department of Anesthesiology, Feinberg School of Medicine Northwestern University Evanston IL

8. Department of Pediatrics University of Texas Southwestern Medical Center Dallas TX

9. Cleveland Clinic Children’s Cleveland OH

Abstract

Background Patients with congenital heart disease (CHD) are at risk for the development of low cardiac output and other physiologic derangements, which could be detected early through continuous stroke volume (SV) measurement. Unfortunately, existing SV measurement methods are limited in the clinic because of their invasiveness (eg, thermodilution), location (eg, cardiac magnetic resonance imaging), or unreliability (eg, bioimpedance). Multimodal wearable sensing, leveraging the seismocardiogram, a sternal vibration signal associated with cardiomechanical activity, offers a means to monitoring SV conveniently, affordably, and continuously. However, it has not been evaluated in a population with significant anatomical and physiological differences (ie, children with CHD) or compared against a true gold standard (ie, cardiac magnetic resonance). Here, we present the feasibility of wearable estimation of SV in a diverse CHD population (N=45 patients). Methods and Results We used our chest‐worn wearable biosensor to measure baseline ECG and seismocardiogram signals from patients with CHD before and after their routine cardiovascular magnetic resonance imaging, and derived features from the measured signals, predominantly systolic time intervals, to estimate SV using ridge regression. Wearable signal features achieved acceptable SV estimation (28% error with respect to cardiovascular magnetic resonance imaging) in a held‐out test set, per cardiac output measurement guidelines, with a root‐mean‐square error of 11.48 mL and R 2 of 0.76. Additionally, we observed that using a combination of electrical and cardiomechanical features surpassed the performance of either modality alone. Conclusions A convenient wearable biosensor that estimates SV enables remote monitoring of cardiac function and may potentially help identify decompensation in patients with CHD.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Cardiology and Cardiovascular Medicine

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